This research is about learning high-level policies of multi-step sequential (MSS) tasks–such as activities of daily living–from demonstrations in a sample efficient manner. This research does not assume access to a simulator or an expert to provide more demonstrations. Learning a task policy in such a setting using state-of-the-art end-to-end approaches is sample inefficient due to a reliance on deep learning frameworks, which are known to require a large amount of data. Besides that, most imitation learning frameworks in robotics assume that a domain expert’s demonstration always contains a correct way of doing the task. Despite its theoretical convenience, this assumption has limited practical value in real-world imitation learning. There are many reasons for an expert in the real world to provide demonstrations that may contain incorrect or potentially unsafe ways of doing a task. To that end, my work proposes a …